使用机器学习来预测2型糖尿病的动脉内膜-介质异常厚度
Chung-Ze Wu1,2, Li-Ying Huang3,4, Fang-Yu Chen4,5
1Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11031, Taiwan.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
机器学习模型在2型糖尿病 (T2D) 患者中有效预测动脉内膜介质厚度 (c-IMT),优于传统方法. 确定的主要风险因素包括年龄,性别和血压对心血管疾病风险.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 糖尿病研究 糖尿病研究
背景情况:
- Carotid intima-media 厚度 (c-IMT) 是心血管疾病 (CVD) 风险的验证标志物,特别是在2型糖尿病 (T2D) 患者中.
- 准确预测c-IMT对于T2D队列中早期心血管疾病风险分层至关重要.
研究的目的:
- 将各种机器学习 (ML) 算法的预测性能与T2D患者c-IMT的传统后勤回归进行比较.
- 为了确定与该队列中c-IMT进展相关的最重要的基线风险因素.
主要方法:
- 一组924名T2D患者被跟踪了四年.
- 机器学习模型 (分类和回归树,随机森林,极端梯度增强,天真贝叶斯) 和多重后勤回归被用来预测c-IMT.
- 模型性能使用接收器操作曲线 (AUC) 下的面积来评估.
主要成果:
- 大多数ML方法在预测c-IMT方面表现出与后勤回归相当或优于后勤回归的性能,这是较高的AUC值所表明的.
- 分类和回归树是唯一的ML方法,不超过后勤回归.
- 关于c-IMT的重要预测因素包括年龄,性别,肌,体重指数,心动血压和糖尿病持续时间.
结论:
- 与传统的物流回归相比,机器学习方法为T2D患者的c-IMT提供了增强的预测能力.
- 这些发现支持将ML整合到改善2型糖尿病心血管风险的早期识别和管理中.
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